
"""simple docstring"""



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					int			     =					AutoencoderKL
    a   :					int			     =					'sample'
    a   :					str			     =					1e-2







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = 4
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = (32, 32)

        _UpperCAmelCase        = floats_tensor((batch_size, num_channels) + sizes  ).to(__UpperCamelCase  )

        return {"sample": image}







    @property
    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        return (3, 32, 32)







    @property
    def   _snake_case       (      self      :							str  )		->Dict:






        '''simple docstring'''






        return (3, 32, 32)







    def   _snake_case       (      self      :							Dict  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = {
            """block_out_channels""": [32, 64],
            """in_channels""": 3,
            """out_channels""": 3,
            """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
            """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
            """latent_channels""": 4,
        }
        _UpperCAmelCase        = self.dummy_input
        return init_dict, inputs_dict







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        pass







    def   _snake_case       (      self      :							int  )		->Dict:






        '''simple docstring'''






        pass







    @unittest.skipIf(torch_device == """mps"""  ,       """Gradient checkpointing skipped on MPS"""  )
    def   _snake_case       (      self      :							Union[str, Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.prepare_init_args_and_inputs_for_common()
        _UpperCAmelCase        = self.model_class(**__UpperCamelCase  )
        model.to(__UpperCamelCase  )

        assert not model.is_gradient_checkpointing and model.training

        _UpperCAmelCase        = model(**__UpperCamelCase  ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model.zero_grad()

        _UpperCAmelCase        = torch.randn_like(__UpperCamelCase  )
        _UpperCAmelCase        = (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        _UpperCAmelCase        = self.model_class(**__UpperCamelCase  )
        # clone model
        model_a.load_state_dict(model.state_dict()  )
        model_a.to(__UpperCamelCase  )
        model_a.enable_gradient_checkpointing()

        assert model_a.is_gradient_checkpointing and model_a.training

        _UpperCAmelCase        = model_a(**__UpperCamelCase  ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model_a.zero_grad()
        _UpperCAmelCase        = (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1e-5  )
        _UpperCAmelCase        = dict(model.named_parameters()  )
        _UpperCAmelCase        = dict(model_a.named_parameters()  )
        for name, param in named_params.items():
            self.assertTrue(torch_all_close(param.grad.data  ,       named_params_a[name].grad.data  ,       atol=5e-5  )  )







    def   _snake_case       (      self      :							int  )		->int:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""  ,       output_loading_info=__UpperCamelCase  )
        self.assertIsNotNone(__UpperCamelCase  )
        self.assertEqual(len(loading_info["""missing_keys"""]  )  ,       0  )

        model.to(__UpperCamelCase  )
        _UpperCAmelCase        = model(**self.dummy_input  )

        assert image is not None, "Make sure output is not None"







    def   _snake_case       (      self      :							Tuple  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""  )
        _UpperCAmelCase        = model.to(__UpperCamelCase  )
        model.eval()

        if torch_device == "mps":
            _UpperCAmelCase        = torch.manual_seed(0  )
        else:
            _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(0  )

        _UpperCAmelCase        = torch.randn(
            1  ,       model.config.in_channels  ,       model.config.sample_size  ,       model.config.sample_size  ,       generator=torch.manual_seed(0  )  ,       )
        _UpperCAmelCase        = image.to(__UpperCamelCase  )
        with torch.no_grad():
            _UpperCAmelCase        = model(__UpperCamelCase  ,       sample_posterior=__UpperCamelCase  ,       generator=__UpperCamelCase  ).sample

        _UpperCAmelCase        = output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
            _UpperCAmelCase        = torch.tensor(
                [
                    -4.0078e-01,
                    -3.8323e-04,
                    -1.2681e-01,
                    -1.1462e-01,
                    2.0095e-01,
                    1.0893e-01,
                    -8.8247e-02,
                    -3.0361e-01,
                    -9.8644e-03,
                ]  )
        elif torch_device == "cpu":
            _UpperCAmelCase        = torch.tensor(
                [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]  )
        else:
            _UpperCAmelCase        = torch.tensor(
                [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]  )

        self.assertTrue(torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       rtol=1e-2  )  )







@slow
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Dict  )		->str:






        '''simple docstring'''






        return f"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCamelCase  ) for s in shape]  )}.npy"""







    def   _snake_case       (      self      :							List[str]  )		->Optional[Any]:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Dict=0  ,       __UpperCamelCase      :							str=(4, 3, 5_12, 5_12)  ,       __UpperCamelCase      :							Optional[Any]=False  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = torch.floataa if fpaa else torch.floataa
        _UpperCAmelCase        = torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCamelCase  ,       __UpperCamelCase  )  )  ).to(__UpperCamelCase  ).to(__UpperCamelCase  )
        return image







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							List[str]="CompVis/stable-diffusion-v1-4"  ,       __UpperCamelCase      :							List[str]=False  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = """fp16""" if fpaa else None
        _UpperCAmelCase        = torch.floataa if fpaa else torch.floataa

        _UpperCAmelCase        = AutoencoderKL.from_pretrained(
            __UpperCamelCase  ,       subfolder="""vae"""  ,       torch_dtype=__UpperCamelCase  ,       revision=__UpperCamelCase  ,       )
        model.to(__UpperCamelCase  ).eval()

        return model







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							List[Any]=0  )		->int:






        '''simple docstring'''






        if torch_device == "mps":
            return torch.manual_seed(__UpperCamelCase  )
        return torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]  )
    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model()
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  )
        _UpperCAmelCase        = self.get_generator(__UpperCamelCase  )

        with torch.no_grad():
            _UpperCAmelCase        = model(__UpperCamelCase  ,       generator=__UpperCamelCase  ,       sample_posterior=__UpperCamelCase  ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase        = sample[-1, -2:, -2:, :2].flatten().float().cpu()
        _UpperCAmelCase        = torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice  )

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=3e-3  )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
            [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
            # fmt: on
        ]  )
    @require_torch_gpu
    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							List[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model(fpaa=__UpperCamelCase  )
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  ,       fpaa=__UpperCamelCase  )
        _UpperCAmelCase        = self.get_generator(__UpperCamelCase  )

        with torch.no_grad():
            _UpperCAmelCase        = model(__UpperCamelCase  ,       generator=__UpperCamelCase  ,       sample_posterior=__UpperCamelCase  ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase        = sample[-1, -2:, :2, -2:].flatten().float().cpu()
        _UpperCAmelCase        = torch.tensor(__UpperCamelCase  )

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-2  )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]  )
    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Union[str, Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model()
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  )

        with torch.no_grad():
            _UpperCAmelCase        = model(__UpperCamelCase  ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase        = sample[-1, -2:, -2:, :2].flatten().float().cpu()
        _UpperCAmelCase        = torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice  )

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=3e-3  )







    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
            [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
            # fmt: on
        ]  )
    @require_torch_gpu
    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model()
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  ,       shape=(3, 4, 64, 64)  )

        with torch.no_grad():
            _UpperCAmelCase        = model.decode(__UpperCamelCase  ).sample

        assert list(sample.shape  ) == [3, 3, 5_12, 5_12]

        _UpperCAmelCase        = sample[-1, -2:, :2, -2:].flatten().cpu()
        _UpperCAmelCase        = torch.tensor(__UpperCamelCase  )

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-3  )







    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
            [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
            # fmt: on
        ]  )
    @require_torch_gpu
    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model(fpaa=__UpperCamelCase  )
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  ,       shape=(3, 4, 64, 64)  ,       fpaa=__UpperCamelCase  )

        with torch.no_grad():
            _UpperCAmelCase        = model.decode(__UpperCamelCase  ).sample

        assert list(sample.shape  ) == [3, 3, 5_12, 5_12]

        _UpperCAmelCase        = sample[-1, -2:, :2, -2:].flatten().float().cpu()
        _UpperCAmelCase        = torch.tensor(__UpperCamelCase  )

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=5e-3  )







    @parameterized.expand([(13,), (16,), (27,)]  )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()  ,       reason="""xformers is not required when using PyTorch 2.0."""  )
    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Dict  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model(fpaa=__UpperCamelCase  )
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  ,       shape=(3, 4, 64, 64)  ,       fpaa=__UpperCamelCase  )

        with torch.no_grad():
            _UpperCAmelCase        = model.decode(__UpperCamelCase  ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            _UpperCAmelCase        = model.decode(__UpperCamelCase  ).sample

        assert list(sample.shape  ) == [3, 3, 5_12, 5_12]

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-1  )







    @parameterized.expand([(13,), (16,), (37,)]  )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()  ,       reason="""xformers is not required when using PyTorch 2.0."""  )
    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							Dict  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model()
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  ,       shape=(3, 4, 64, 64)  )

        with torch.no_grad():
            _UpperCAmelCase        = model.decode(__UpperCamelCase  ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            _UpperCAmelCase        = model.decode(__UpperCamelCase  ).sample

        assert list(sample.shape  ) == [3, 3, 5_12, 5_12]

        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-2  )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
            [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
            # fmt: on
        ]  )
    def   _snake_case       (      self      :							Any  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Optional[Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_sd_vae_model()
        _UpperCAmelCase        = self.get_sd_image(__UpperCamelCase  )
        _UpperCAmelCase        = self.get_generator(__UpperCamelCase  )

        with torch.no_grad():
            _UpperCAmelCase        = model.encode(__UpperCamelCase  ).latent_dist
            _UpperCAmelCase        = dist.sample(generator=__UpperCamelCase  )

        assert list(sample.shape  ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        _UpperCAmelCase        = sample[0, -1, -3:, -3:].flatten().cpu()
        _UpperCAmelCase        = torch.tensor(__UpperCamelCase  )

        _UpperCAmelCase        = 3e-3 if torch_device != """mps""" else 1e-2
        assert torch_all_close(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=__UpperCamelCase  )

"""simple docstring"""



def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    if not isinstance(_A       ,   _A   ) or number < 0:
        raise ValueError("""Input must be a non-negative integer"""   )

    _UpperCAmelCase        = 0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
 import doctest

 doctest.testmod()